Mobilising global citizenship education for alternative futures in challenging times: an introduction
Bibliographic record
Abstract
This article introduces the Special Issue ‘Mobilising Global Citizenship Education for Alternative Futures in Challenging Times’. Our call for papers was inspired by the 10th anniversary of a 2011 special issue on the political economy of global citizenship education (GCE) edited by Vanessa Andreotti. In this introduction, we review the main aims of this follow-up collection, noting the proliferation of scholarship on the topic of GCE and calls for more critical engagements. We outline the key provocations of the call for papers and review the contributions, setting out an overview of the landscape of the SI through intersecting themes, concerns, and possibilities. The articles in this collection demonstrate ongoing concerns around neoliberalism and how GCE is always implicated in the inequalities it aims to address. They also highlight emerging possibilities and further questions in response to current conditions. This collection contributes and maps new edges of debate within critical approaches, provides practical insights into reflexive approaches in situ, and sheds light on common and specific issues and possibilities across regions and across formal and nonformal educational contexts. We end the introduction by highlighting the contribution from Vanessa Andreotti that concludes the collection and articulates a ‘passing of the torch’ to those entering and continuing work in this dynamic field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".